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  Subjects -> STATISTICS (Total: 130 journals)
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Journal of the Royal Statistical Society Series C (Applied Statistics)
Journal Prestige (SJR): 1.982
Citation Impact (citeScore): 2
Number of Followers: 37  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0035-9254 - ISSN (Online) 1467-9876
Published by John Wiley and Sons Homepage  [1763 journals]
  • Bayesian imputation of COVID‐19 positive test counts for nowcasting
           under reporting lag

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      Authors: Radka Jersakova; James Lomax, James Hetherington, Brieuc Lehmann, George Nicholson, Mark Briers, Chris Holmes
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 834-860, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12557
      Issue No: Vol. 71, No. 4 (2022)
       
  • A Bayesian multi‐region radial composite reservoir model for
           deconvolution in well test analysis

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      Authors: Themistoklis Botsas; Jonathan A. Cumming, Ian H. Jermyn
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 951-968, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12562
      Issue No: Vol. 71, No. 4 (2022)
       
  • Analysing cycling sensors data through ordinal logistic regression with
           functional covariates

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      Authors: Julien Jacques; Sanja Samardžić
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 969-986, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12563
      Issue No: Vol. 71, No. 4 (2022)
       
  • Modelling the extremes of seasonal viruses and hospital congestion: The
           example of flu in a Swiss hospital

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      Authors: Setareh Ranjbar; Eva Cantoni, Valérie Chavez‐Demoulin, Giampiero Marra, Rosalba Radice, Katia Jaton
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 884-905, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12559
      Issue No: Vol. 71, No. 4 (2022)
       
  • Multifidelity computer model emulation with high‐dimensional output: An
           application to storm surge

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      Authors: Pulong Ma; Georgios Karagiannis, Bledar A. Konomi, Taylor G. Asher, Gabriel R. Toro, Andrew T. Cox
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 861-883, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12558
      Issue No: Vol. 71, No. 4 (2022)
       
  • Highly irregular functional generalized linear regression with electronic
           health records

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      Authors: Justin Petrovich; Matthew Reimherr, Carrie Daymont
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 806-833, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12556
      Issue No: Vol. 71, No. 4 (2022)
       
  • The modelling of movement of multiple animals that share behavioural
           features

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      Authors: Gianluca Mastrantonio
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 932-950, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12561
      Issue No: Vol. 71, No. 4 (2022)
       
  • Improving cardio‐mechanic inference by combining in vivo strain data
           with ex vivo volume–pressure data

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      Authors: Alan Lazarus; Hao Gao, Xiaoyu Luo, Dirk Husmeier
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 906-931, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12560
      Issue No: Vol. 71, No. 4 (2022)
       
  • Exploring British accents: Modelling the trap–bath split with
           functional data analysis

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      Authors: Aranya Koshy; Shahin Tavakoli
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 773-805, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12555
      Issue No: Vol. 71, No. 4 (2022)
       
  • A nearest‐neighbour Gaussian process spatial factor model for censored,
           multi‐depth geochemical data

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      Authors: Tilman M. Davies; Sudipto Banerjee, Adam P. Martin, Rose E. Turnbull
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 1014-1043, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12565
      Issue No: Vol. 71, No. 4 (2022)
       
  • Reliability analysis of artificial intelligence systems using recurrent
           events data from autonomous vehicles

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      Authors: Jie Min; Yili Hong, Caleb B. King, William Q. Meeker
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 987-1013, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12564
      Issue No: Vol. 71, No. 4 (2022)
       
  • A unifying framework for flexible excess hazard modelling with
           applications in cancer epidemiology

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      Authors: Alessia Eletti; Giampiero Marra, Manuela Quaresma, Rosalba Radice, Francisco Javier Rubio
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 1044-1062, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12566
      Issue No: Vol. 71, No. 4 (2022)
       
  • Issue Information

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      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 71, Issue 4, Page 771-772, August 2022.
      PubDate: 2022-08-16T09:50:10-07:00
      DOI: 10.1111/rssc.12506
      Issue No: Vol. 71, No. 4 (2022)
       
  • Optimal approximate choice designs for a two‐step coffee choice, taste
           and choice again experiment

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      Authors: Nedka Dechkova Nikiforova; Rossella Berni, Jesús Fernando López‐Fidalgo
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-10-03T09:10:21-07:00
      DOI: 10.1111/rssc.12601
       
  • Flexible domain prediction using mixed effects random forests

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      Authors: Patrick Krennmair; Timo Schmid
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-10-02T08:19:42-07:00
      DOI: 10.1111/rssc.12600
       
  • A Bayesian model for estimating Sustainable Development Goal indicator
           4.1.2: School completion rates

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      Authors: Ameer Dharamshi; Bilal Barakat, Leontine Alkema, Manos Antoninis
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-25T08:45:19-07:00
      DOI: 10.1111/rssc.12595
       
  • Efficient estimation of the marginal mean of recurrent events

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      Authors: Giuliana Cortese; Thomas H. Scheike
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-21T11:10:47-07:00
      DOI: 10.1111/rssc.12586
       
  • Sequential one‐step estimator by sub‐sampling for customer churn
           analysis with massive data sets

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      Authors: Feifei Wang; Danyang Huang, Tianchen Gao, Shuyuan Wu, Hansheng Wang
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-19T09:22:14-07:00
      DOI: 10.1111/rssc.12597
       
  • The saturated pairwise interaction Gibbs point process as a joint species
           distribution model

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      Authors: Ian Flint; Nick Golding, Peter Vesk, Yan Wang, Aihua Xia
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-19T09:17:31-07:00
      DOI: 10.1111/rssc.12596
       
  • Contour models for physical boundaries enclosing star‐shaped and
           approximately star‐shaped polygons

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      Authors: Hannah M. Director; Adrian E. Raftery
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-19T09:10:33-07:00
      DOI: 10.1111/rssc.12592
       
  • Score test for assessing the conditional dependence in latent class models
           and its application to record linkage

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      Authors: Huiping Xu; Xiaochun Li, Zuoyi Zhang, Shaun Grannis
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-18T08:19:27-07:00
      DOI: 10.1111/rssc.12590
       
  • Leveraging network structure to improve pooled testing efficiency

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      Authors: Daniel K. Sewell
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-16T02:22:18-07:00
      DOI: 10.1111/rssc.12594
       
  • Semi‐parametric time‐to‐event modelling of lengths
           of hospital stays

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      Authors: Yang Li; Hao Liu, Xiaoshen Wang, Wanzhu Tu
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-15T12:16:47-07:00
      DOI: 10.1111/rssc.12593
       
  • Utility‐based Bayesian personalized treatment selection for advanced
           breast cancer

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      Authors: Juhee Lee; Peter F. Thall, Bora Lim, Pavlos Msaouel
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-09T08:33:43-07:00
      DOI: 10.1111/rssc.12582
       
  • Measuring diachronic sense change: New models and Monte Carlo methods
           for Bayesian inference

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      Authors: Schyan Zafar; Geoff K. Nicholls
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-09-06T09:05:51-07:00
      DOI: 10.1111/rssc.12591
       
  • Environmental Engel curves: A neural network approach

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      Authors: Tullio Mancini; Hector Calvo‐Pardo, Jose Olmo
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-31T11:15:20-07:00
      DOI: 10.1111/rssc.12588
       
  • Non‐parametric Bayesian covariate‐dependent multivariate functional
           clustering: An application to time‐series data for multiple air
           pollutants

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      Authors: Daewon Yang; Taeryon Choi, Eric Lavigne, Yeonseung Chung
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-30T12:54:54-07:00
      DOI: 10.1111/rssc.12589
       
  • A model‐based approach to predict employee compensation components

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      Authors: Andreea L. Erciulescu; Jean D. Opsomer
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-26T12:39:39-07:00
      DOI: 10.1111/rssc.12587
       
  • Investigating the association of a sensitive attribute with a random
           variable using the Christofides generalised randomised response design and
           Bayesian methods

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      Authors: Shen‐Ming Lee; Truong‐Nhat Le, Phuoc‐Loc Tran, Chin‐Shang Li
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-16T07:32:04-07:00
      DOI: 10.1111/rssc.12585
       
  • Statistical integration of heterogeneous omics data: Probabilistic
           two‐way partial least squares (PO2PLS)

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      Authors: Said el Bouhaddani; Hae‐Won Uh, Geurt Jongbloed, Jeanine Houwing‐Duistermaat
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-16T06:43:45-07:00
      DOI: 10.1111/rssc.12583
       
  • Modelling time‐varying rankings with autoregressive and
           score‐driven dynamics

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      Authors: Vladimír Holý; Jan Zouhar
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-08-02T10:35:01-07:00
      DOI: 10.1111/rssc.12584
       
  • Network Hawkes process models for exploring latent hierarchy in social
           animal interactions

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      Authors: Owen G. Ward; Jing Wu, Tian Zheng, Anna L. Smith, James P. Curley
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-07-28T08:39:22-07:00
      DOI: 10.1111/rssc.12581
       
  • Robust correspondence analysis

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      Authors: Marco Riani; Anthony C. Atkinson, Francesca Torti, Aldo Corbellini
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-07-27T11:11:52-07:00
      DOI: 10.1111/rssc.12580
       
  • Spatiotemporal ETAS model with a renewal main‐shock arrival process

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      Authors: Tom Stindl; Feng Chen
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-07-26T12:00:00-07:00
      DOI: 10.1111/rssc.12579
       
  • Specification analysis for technology use and teenager well‐being:
           Statistical validity and a Bayesian proposal

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      Authors: Christoph Semken; David Rossell
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-07-13T10:45:23-07:00
      DOI: 10.1111/rssc.12578
       
  • Heterogeneous graphical model for non‐negative and
           non‐Gaussian PM2.5 data

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      Authors: Jiaqi Zhang; Xinyan Fan, Yang Li, Shuangge Ma
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-22T01:32:47-07:00
      DOI: 10.1111/rssc.12575
       
  • A Bayesian spatio‐temporal analysis of markets during the Finnish
           1860s famine

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      Authors: Tiia‐Maria Pasanen; Miikka Voutilainen, Jouni Helske, Harri Högmander
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-20T02:40:21-07:00
      DOI: 10.1111/rssc.12577
       
  • Nowcasting COVID‐19 deaths in England by age and region

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      Authors: Shaun R. Seaman; Pantelis Samartsidis, Meaghan Kall, Daniela De Angelis
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-15T12:00:00-07:00
      DOI: 10.1111/rssc.12576
       
  • Unconventional policies effects on stock market volatility: The MAP
           approach

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      Authors: Demetrio Lacava; Giampiero M. Gallo, Edoardo Otranto
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-14T12:00:00-07:00
      DOI: 10.1111/rssc.12574
       
  • Multilevel network item response modelling for discovering differences
           between innovation and regular school systems in Korea

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      Authors: Ick Hoon Jin; Minjeong Jeon, Michael Schweinberger, Jonghyun Yun, Lizhen Lin
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-13T07:45:03-07:00
      DOI: 10.1111/rssc.12569
       
  • Stopping time detection of wood panel compression: A functional
           time‐series approach

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      Authors: Han Lin Shang; Jiguo Cao, Peijun Sang
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-06-07T12:00:00-07:00
      DOI: 10.1111/rssc.12572
       
  • Bayesian nonparametric modelling of multiple graphs with an application to
           ethnic metabolic differences

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      Authors: Marco Molinari; Andrea Cremaschi, Maria De Iorio, Nishi Chaturvedi, Alun D. Hughes, Therese Tillin
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-05-28T04:44:13-07:00
      DOI: 10.1111/rssc.12570
       
  • Dynamic disease screening by joint modelling of survival and longitudinal
           data

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      Authors: Peihua Qiu; Lu You
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-05-27T04:20:54-07:00
      DOI: 10.1111/rssc.12573
       
  • Assessing predictive discrimination performance of biomarkers in the
           presence of treatment‐induced dependent censoring

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      Authors: Cuihong Zhang; Jing Ning, Steven H. Belle, Robert H. Squires, Jianwen Cai, Ruosha Li
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-05-25T05:28:28-07:00
      DOI: 10.1111/rssc.12571
       
  • Non‐separable spatio‐temporal models via transformed multivariate
           Gaussian Markov random fields

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      Authors: Marcos O. Prates; Douglas R. M. Azevedo, Ying C. MacNab, Michael R. Willig
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-05-23T08:04:07-07:00
      DOI: 10.1111/rssc.12567
       
  • Classification of myocardial blood flow based on dynamic
           contrast‐enhanced magnetic resonance imaging using hierarchical Bayesian
           models

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      Authors: Yalei Yang; Hao Gao, Colin Berry, David Carrick, Aleksandra Radjenovic, Dirk Husmeier
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-05-12T12:00:00-07:00
      DOI: 10.1111/rssc.12568
       
  • A principal stratification approach to estimating the effect of continuing
           treatment after observing early outcomes

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      Authors: Patrick M. Schnell; Richard Baumgartner, Shahrul Mt‐Isa, Vladimir Svetnik
      Abstract: Journal of the Royal Statistical Society: Series C (Applied Statistics), EarlyView.
      PubDate: 2022-04-07T07:04:33-07:00
      DOI: 10.1111/rssc.12552
       
 
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